Interharmonics Suppression Scheme in PV System with Variable Step Size MPPT Algorithm

By detection of input signal, Maximum Power Point Tracking (MPPT) algorithms are used to maximize the potential output power. Since Perturbation and Observe (P&O) method was first applied in a Photovoltaic (PV) system, a myriad of MPPT algorithms has been proposed. With the pros and cons of various MPPT algorithms, a detailed analysis of several typical direct search and module-integrated MPPT algorithms is presented in this paper. Directions of future work for implementing new MPPT algorithms are also outlined.

PV irrigation systems have begun to be used intensively today, as energy needs increase. In Partially Shaded Conditions (PSC), the efficiency of the PV system decreases significantly, and traditional Maximum Power Point Tracking (MPPT) algorithms become insufficient. On the other hand, traditional MPPT algorithms require sensors to measure the current and voltage of the PV system. In this study, a sensorless hybrid MPPT algorithm is proposed to reduce system costs and enable operation without the need for PV system data. A simulation study was conducted in the MATLAB/Simulink environment to examine the PV system. The proposed algorithm has been tested under four different PSC scenarios. PV system power, motor speed, and currents were examined under each condition. The high maximum power tracking performance of the proposed algorithm is demonstrated through simulation results. In the steady state, the lowest MPPT efficiency was 95.66%, whereas the highest MPPT efficiency was 99.9%. The MPPT algorithm completed in less than 2 seconds, with the first stage taking 1.3 seconds to reach most of the maximum PV system power. The second stage of the MPPT algorithm was used to achieve maximum power in a narrower area.

This paper presents a performance analysis of grid connected SPV system for different maximum power point tracking (MPPT) algorithms. The detailed mathematical model of grid connected three-phase solar photovoltaic (SPV) system with parametric model of SPV cell, thermal modelling and switching loss calculation of switching devices are discussed. The performance evaluation has been carried out for perturb-and-observe (P&O) and incremental conductance (IC)-based MPPT algorithms for various outputs of the SPV array, in terms of energy injected to grid, switching losses, junction temperature and sink temperature, for switching in the DC-DC boost converter. The results validated the effectiveness of the MPPT algorithm in increasing SPV output energy, decrease in the switching losses, junction and sink temperature. The results show that IC method is slightly better to P&O method in energy injected to the grid with lower switching losses, junction and sink temperature.

Photovoltaic (PV) systems are increasingly used as a sustainable energy source, but their efficiency is significantly affected by environmental factors such as shading and temperature variations. This study proposes a novel Maximum Power Point Tracking (MPPT) algorithm based on the fusion of Support Vector Regression (SVR) and Incremental Conductance (INC) techniques, designed to enhance the performance of PV systems under partial shading conditions. The SVR model is employed to predict the power output of the PV system based on voltage and current measurements, leveraging its capability to handle non-linear relationships with high accuracy. The INC method is then integrated with the SVR predictions to dynamically adjust the operating point of the PV array, ensuring rapid and precise tracking of the maximum power point (MPP). The algorithm was implemented in MATLAB, and comprehensive simulations were conducted to evaluate its performance. Simulation results demonstrate that the SVR-INC fusion algorithm significantly improves the accuracy and stability of MPPT under varying shading conditions. The algorithm effectively reduces the tracking time to the MPP and minimizes power losses caused by partial shading. Compared to traditional MPPT methods, the proposed algorithm shows enhanced robustness and faster convergence, making it highly suitable for practical applications in real-world PV systems. Furthermore, the integration of SVR with INC provides a robust framework for dealing with the complexities of $P V$ power generation. The algorithm’s ability to adapt to changing environmental conditions ensures that the PV system operates at its maximum efficiency. The study also discusses the optimization of SVR parameters and the importance of datadriven approaches in enhancing MPPT performance. In conclusion, the proposed SVR-INC fusion MPPT algorithm offers a promising solution for improving the efficiency and reliability of PV systems under partial shading. The findings highlight the potential of this approach for future advancements in PV technology, contributing to the development of more efficient and resilient solar energy systems..

In order to operate a solar photovoltaic (PV) system at its maximum power point (MPP) under numerous weather conditions, it is necessary to achieve uninterrupted optimal power production and to minimize energy losses, energy generation cost, and payback time. Under partial shading conditions (PSC), the formation of multiple peaks in the power voltage characteristic curve of a PV cell puzzles conventional MPP tracking (MPPT) algorithms trying to identify the global MPP (GMPP). Meanwhile, soft-computing MPPT algorithms can identify the GMPP even under PSC. Drawbacks such as structural complexity, computational complexity, huge memory requirements, and difficult implementation all affect the viability of soft-computing algorithms. However, those drawbacks have been successfully overcome with a novel ten check algorithm (TCA). To improve the performance of the TCA in terms of MPPT speed and efficiency, a novel concept of data arrangement is introduced in this paper. The proposed structure is referred to as Optimized TCA (OTCA). A comparison of the proposed OTCA and classic TCA algorithms was conducted for standard benchmarks. The results proved the superiority of the OTCA algorithm compared to both TCA and flower pollination (FPA) algorithms. The major advantage of OTCA in MPPT stems from its speed as compared to TCA and FPA, with almost 86% and 90% improvement, respectively.

This paper presents the analysis and operation of a grid connected photovoltaic (PV)energy conversion system with an Adaptive Neuro-Fuzzy Inference System (ANFIS)based maximum power point tracking (MPPT)algorithm. Particle swarm optimization is used to train the membership functions while the least squares algorithm is used to update the consequent parameters of the ANFIS with changing operating condition of PV solar system. The MPPT algorithm maximizes conversion efficiency by adjusting the duty cycle of the buck boost converter to change the output voltage of the solar panel and hence achieving the maximum panel output power for a given set of environmental conditions. The ANFIS is trained by using a hybrid algorithm implementing least squares estimator and particle swarm optimization with data obtained by operating the system using the Perturb and Observe (P&O)MPPT algorithm. The performance of the proposed ANFIS based MPPT algorithm is validated in simulation using MATLAB/Simulink at different operating conditions. It is proven that the designed ANFIS based MPPT scheme achieves a very fast response with little oscillations while transferring maximum power from solar panel to the grid line as compared to the conventional P&O based MPPT scheme.

A comparative study between frequently used maximum power point tracking (MPPT) algorithms and a proposed Least Squares Support Vector Machine (LS-SVM) based MPPT algorithm under rapidly changing insolation condition (RCIC) is presented in this paper. The comparison shows that the proposed LS-SVM based MPPT algorithm offers some advantages over existing MPPT algorithms under RCIC, namely elimination of ripples in the output voltage of a PV solar system as well as improved tracking accuracy. The algorithms have been tested through simulation studies carried out on real data recorded by the Australia Bureau of Meteorology. The study compares the performances of LS-SVM, P&O and IncCond.

Maximum Power Point Tracking (MPPT) algorithms are essential in solar photovoltaic (PV) generation. In the last years, a major research effort has been made in order to get more efficient MPPT algorithms. However, in the literature, there is not a unique methodology for testing and comparing these kind of algorithms when facing dynamic irradiance conditions. This paper proposes a systematic way of evaluating and comparing MPPT algorithms for PV systems, at the simulation stage, unifying the following aspects: the PV system configuration, the type of simulation to be performed, and the dynamic irradiance tests to take into account. The MPPT algorithm is implemented into the dc-dc converter of the system for simplification purposes, simulations are carried out through state-space equations in order to reduce the total simulation time, and three irradiance dynamic tests are used systematically to provide full comparison under different environmental conditions. To test the flexibility of the proposed technique, a comparison of three commonly known Perturb and Observe (P&O) algorithms has been performed, giving special attention to the MPPT drift phenomenon. All interested researchers can request a computer application that implements the proposed methodology.

In this study, a review on several maximum power point tracking (MPPT) algorithms is given. MPPT algorithms have three major categories: Conventional methods, Advanced Soft Computing methods and Hybrid algorithms. MPPT algorithms are significant to operate the photovoltaic energy conversion systems as close as possible to the MPP resulting in photovoltaic (PV) arrays with high efficiency. In this review, the MPPT algorithms implementation complexity, the complete procedure and its effects in the PV output were given with special attention to the sensors used in the system in the first portion. Further, different MPPT algorithms for PV systems are highlighted with examples, it's parameters like complexity, and sensors used are described. In the end, the MPPT algorithms for PV systems were compared, reviewed and reported. The ultimatum of this work is to provide a survey reference for users of PV based power generation and valued information for researchers of this particular field.

Currently, Photovoltaic (PV) energy is the most important energy resource in electrical power applications around the world since it is inexhaustible, clean and pollution free. As the output power from the PV arrays changing with solar irradiance and temperature, so it is necessary to operate the photovoltaic (PV) system at the maximum power point (MPP) to increase the power extraction from the solar panel. For this effective operation and maximum power extraction from a PV system, Maximum power point tracking (MPPT) algorithm plays a vital role by operating the PV energy system near the MPP. In this work, amongst several existing well established MPPT algorithms, the open voltage (OV) based algorithm is chosen for simulation of a PV system using Matlab / Simscape. As maximum power point (MPP) can be attained with a certain amount of load from the PV power system, so load matching is an important concept of MPPT algorithms. Here, the performances of this open voltage (OV) based algorithm method is evaluated for different values of solar radiation and battery was used as load to see the charging performance for the algorithm.

Maximum power point tracking (MPPT) algorithms are invariably employed to utilize solar photovoltaic (PV) systems effectively. Perturbation based MPPT algorithms are popular due to their simplicity and reasonable efficiency. While novel MPPT algorithms claim increased energy utilization over classic perturbation techniques, their performance is governed by the choice of optimal algorithm parameters. Existing guidelines for parameter optimization are mathematically laborious and are not generic. Hence, this paper aims to provide simple and comprehensive guidelines to ensure optimal performance from the perturbation based MPPT technique. For an illustration of proposed claims, a solar PV fed boost converter is investigated to examine the effect of input capacitor, digital filter cut-off frequency, system time constant and sampling time on implementing a classic Perturb and Observe (P and O) algorithm. The readers will be presented with two simple step tests (to determine the effective system time constant) and proposed guidelines to choose the optimal performance sampling time. Necessary laboratory experiments show that an appropriate choice of sampling time could increase efficiency and ensure system stability. This investigation’s learnings can be easily extended to any power electronics converter, loads and all perturbation-based algorithms used in solar PV systems. The results of appropriate tests on the system’s mathematical model and the laboratory prototype are presented in detail to support this research’s claims.

Recently, real-time simulation of renewable energy sources are indispensible for evaluating the performance of the maximum power point tracking (MPPT) controller, especially in the photovoltaic (PV) system in order to reduce cost in the testing phase. Nowadays, real time PV simulators are obtained by using analog and/or digital components. In this paper, a real-time simulation of a PV system with a boost converter was proposed using only the digital signal processor (DSP) processor with two DC voltage sources to emulate the temperature and irradiation in the PV system. AMATLAB/ Simulink environment was used to develop the real-time PV system with a boost converter into a C-program and build it into a DSP controller TMS320F28335. Besides, the performance of the real-time DSP-based PV was tested in different temperature and irradiation conditions to observe the P-V and V-I characteristics. Further, the performance of the PV with a boost converter was tested at different temperatures and irradiations using MPPT algorithms. This scheme was tested through simulation and the results were validated with that of standard conditions given in the PV data sheets. Implementation of this project helped to attract more researchers to study renewable energy applications without real sources. This might facilitate the study of PV systems in a real-time scenario and the evaluation of what should be expected for PV modules available in the market.

A software tool for testing maximum power point tracking algorithms using real working conditions

This paper presents an event-triggered approach to optimally implement a Maximum Power Point Tracking (MPPT) algorithm into a Digital Signal Processor (DSP). The proposed method allows improving the amount and distribution of time required for executing control tasks. The used nested loop control architecture has an outer loop of MPPT generating the conductance reference used by an inner loop which regulates the input conductance of a DC-DC converter. This last loop enforces a sliding-mode loss-free-resistor behavior for the power converter by means of a simple hysteresis comparator. Computations required by the MPPT algorithm are synchronously executed by the two possible commutation events produced by the inner loop during a switching period. Then, the acquisition of signals must be activated only at an instant before each one of the switching events, releasing the most of the time to implement other tasks. This last characteristic and the use of a nested loop control architecture facilitate the integration of the other essential control functions for photovoltaic (PV) generators in microgrids. Simulation and experimental results confirm the high potetialities of this implementation approach.

This paper presents a comprehensive comparative study between two prominent Maximum Power Point Tracking (MPPT) algorithms: the Perturb and Observe (P&O) method and the Incremental Conductance method (IC). The study delves into their operational principles, efficiency, robustness, implementation complexity, response time, and sensitivity to parameter changes. Through theoretical analysis and numerical simulations, the strengths and limitations of each algorithm are thoroughly assessed, offering valuable insights for optimizing photovoltaic (PV) systems. These simulations utilize established mathematical models of PV systems and MPPT algorithms. The findings reveal nuanced differences between the P&O and Incremental Conductance methods. Incremental Conductance demonstrates superior efficiency, particularly in environments with dynamic irradiance levels and partial shading conditions, owing to its ability to dynamically adjust the operating point. However, it exhibits increased implementation complexity compared to the simpler and more robust P&O method. In conclusion, this comparative study offers valuable insights into MPPT algorithm optimization for PV systems. While Incremental Conductance excels in efficiency and adaptability, P&O remains a viable option for applications with limited computational resources or stable environmental conditions due to its simplicity and robustness.